What Business Practices Enable Agile Analytics?
Part four in a four-part series.
We’ve mentioned some of the technical innovations that support an Agile approach to analytics; there are also business practices to consider. Some practices in Agile software development apply equally well to analytics as any other project, including the need for a sustainable development pace; close collaboration; face-to-face conversation; motivated and trustworthy contributors, and continuous attention to technical excellence. Additional practices pertinent to analytics include:
- Commitment to open standards architecture
- Rigorous selection of the right tool for the task
- Close collaboration between analysts and IT
- Focus on solving the client’s business problem
More often than not, customers with serious cycle time issues are locked into closed single-vendor architecture. Lacking an open architecture to interface with data at the front end and back end of the analytics workflow, these organizations are forced into treating the analytics tool as a data management tool and decision engine; this is comparable to using a toothbrush to paint your house. Server-based analytic software packages are very good at analytics, but perform poorly as databases and decision engines.
Agile analysts take a flexible, “best-in-class” approach to solving the problem at hand. No single vendor offers “best-in-class” tools for every analytic method and algorithm. Some vendors, like KXEN, offer unique algorithms that are unavailable from other vendors; others, like Salford Systems, have specialized experience and intellectual property that enables them to offer a richer feature set for certain data mining methods. In an Agile analytics environment, analysts freely choose among commercial, open source and homegrown software, using a mashup of tools as needed.
While it may seem like a platitude to call for collaboration between an organization’s analytics community and the IT organization, we frequently see customers who have developed complex processes for analytics that either duplicate existing IT processes, or perform tasks that can be done more efficiently by IT. Analysts should spend their time doing analysis, not data movement, management, enhancement, cleansing or scoring; but surveyed analysts typically report that they spend much of their time performing these tasks. In some cases, this is because IT has failed to provide the needed support; in other cases, the analytics team insists on controlling the process. Regardless of the root cause, IT and analytics leadership alike need to recognize the need for collaboration, and an appropriate division of labor.
Focusing the analytics effort on the client’s business problem is essential for the practice of Agile analytics. Organizations frequently get stuck on issues that are difficult to resolve because the parties are focused on different goals; in the analytics world, this takes the form of debates over tools, methods and procedures. Analysts should bear in mind that clients are not interested in winning prizes for the “best” model, and they don’t care about the analyst’s advanced degrees. Business requires speed, agility and clarity, and analysts who can’t deliver on these expectations will not survive.